Kick Butt in Coding Interviews: Data Structures in Python
Ace coding interviews! Master data structures – a fundamental computer science topic – and boost your problem-solving skills. Learn practical applications now.
Course Content
Kick Butt in Coding Interviews: Data Structures in Python is an intermediate-level Skillshare course taught by Alvin Wan. It runs 2h 58m, is taught in English, and includes a certificate of completion. Learners rate it 5.0 out of 5 from 1 review, with 766 students enrolled. It is a paid course, first published September 2022.
Struggling with coding interviews? Master data structures and unlock your potential! This course dives deep into a fundamental computer science topic crucial for every aspiring programmer.
Here's what you'll learn:
- Algorithm Efficiency: Understand and analyze algorithm performance using orders of growth analysis. Learn to choose the most efficient approach for your coding challenges.
- Core Data Structures: Gain hands-on experience with essential data structures, including stacks, queues, linked lists, trees, and hash maps.
- Data Structure Algorithms: Explore the complexities of common operations like insertion, deletion, search, and access within each data structure.
- Practical Applications: Discover the strengths and weaknesses of different data structures and how they impact real-world applications.
- Interview Preparation: Sharpen your skills with 28 in-depth, interview-style practice questions.
- Ace Your Interviews: Benefit from 20 valuable interview tips to boost your confidence and performance.
This highly interactive course emphasizes coding along, ensuring you grasp the concepts effectively. By the end, you'll possess strong data structures know-how, understand their practical implications, and be well-prepared for coding interviews.
Ready to level up your coding interview game?
Looking for more? Explore my other courses:
- Creative Coding: VR101 (AFrame)
- Data Science & Machine Learning: Coding 101 (Python), OOP 101 (Python), SQL 101 (Database Design), Data 101 (Analytics), Computer Vision 101 (Applied ML)
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